Flight Ticket Fare Prediction A comprehensive machine learning project focused on predicting domestic flight ticket fares based on historical data and key flight attributes.

Project Highlights Purpose: Estimate flight ticket prices using data-driven insights, helping passengers and airline professionals make informed decisions.

Scope: Tailored to domestic flights, capturing pricing dynamics across diverse routes, airlines, schedules, and seasonal trends.

Use Cases:

Passengers: Plan and budget for flights by forecasting fare trends.

Airlines & Agencies: Optimize pricing strategies based on predicted market behavior.

Dataset Overview Based on publicly available “Flight Fare Prediction” datasets (e.g. from Kaggle), typically containing ~10,000+ flight records

Key features:

Airline, Source & Destination

Date_of_Journey, Departure/Arrival Times

Duration, Total_Stops, Additional_Info

Route (if available), any other metadata

Fare/Price – the target variable to predict

Workflow & Methodology Data Exploration & Cleaning Handle missing values, outliers, and inconsistencies.

Feature Engineering Extract meaningful signals from journey dates, durations, arrival/departure times, stops, and more

Modeling Approach Compare regression models like:

Linear Regression

Random Forest

Metrics like MAE, RMSE, R²

Visual checks: Price vs. predicted fare plots, feature importance analysi